code: def step(self, t0, t1, y0, extra0):
      del extra0
      dt = t1 - t0
      I_k = self.bm(t0, t1)
      v = self.v_term(I_k, dt)
  
      if self.options[METHOD_OPTIONS.grad_free]:
          f, g = self.sde.f_and_g(t0, y0)
          g_ = (
              g.squeeze(2) if g.dim() == 3 else g
          )  # scalar noise vs diagonal noise
          sqrt_dt = dt.sqrt()
          # TODO: This y_prime_f_factor looks unnecessary: whether it's there or not we get the correct Taylor
          #  expansion. I've (Patrick) not been able to find a reference making clear why it's sometimes included.
          y0_prime = y0 + self.y_prime_f_factor(dt, f) + g_ * sqrt_dt
          g_prime = self.sde.g(t0, y0_prime)
          g_prod_I_k = self.sde.prod(g, I_k)
          gdg_prod = self.sde.prod(g_prime - g, v) / (2 * sqrt_dt)
      else:
          f = self.sde.f(t0, y0)
          g_prod_I_k, gdg_prod = self.sde.g_prod_and_gdg_prod(
              t0, y0, I_k, 0.5 * v
          )
  
      y1 = y0 + f * dt + g_prod_I_k + gdg_prod
  
      return y1, ()
  
file_path: torchsde/torchsde/_core/methods/milstein.py
imports: import abc
  from ..None import adjoint_sde
  from ..None import base_solver
  from ...settings import SDE_TYPES, NOISE_TYPES, LEVY_AREA_APPROXIMATIONS, METHOD_OPTIONS
label_desc: Use image node_id 4 for calling the BaseMilstein obj's underlying member method code with example usage: obj.step(t0, t1, y0, extra0) and returns: y1,  